Covid Report for Family

Background

Notes and format last updated Apr 16, 2020

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines using rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines using rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	16,515
2	New Jersey	14,702
3	Massachusetts	10,195
4	Rhode Island	9,376
5	Connecticut	8,588
6	District of Columbia	7,540
7	Louisiana	6,452
8	Delaware	5,515
9	Illinois	5,199
10	Maryland	4,485
11	Michigan	4,439
12	Pennsylvania	4,215
13	Nebraska	3,332
14	Iowa	3,204
15	Indiana	3,124
16	South Dakota	3,075
17	Colorado	2,996
18	Mississippi	2,757
19	Georgia	2,719
20	Virginia	2,373
21	Washington	2,081
22	Tennessee	1,993
23	New Mexico	1,973
24	New Hampshire	1,938
25	Kansas	1,923
26	Nevada	1,839
27	Ohio	1,793
28	Florida	1,742
29	Alabama	1,720
30	Utah	1,699
31	North Dakota	1,661
32	California	1,489
33	Wisconsin	1,471
34	Missouri	1,453
35	Vermont	1,453
36	Minnesota	1,392
37	South Carolina	1,328
38	Kentucky	1,303
39	Arizona	1,278
40	Texas	1,182
41	North Carolina	1,171
42	Arkansas	1,168
43	Idaho	1,096
44	Oklahoma	1,042
45	Maine	912
46	Wyoming	780
47	West Virginia	693
48	Oregon	673
49	Puerto Rico	602
50	Alaska	504
51	Hawaii	434
52	Montana	426

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	New Jersey	258
2	District of Columbia	247
3	Rhode Island	202
4	Illinois	194
5	Massachusetts	193
6	Nebraska	191
7	Iowa	155
8	Maryland	145
9	New York	141
10	Connecticut	124
11	Delaware	113
12	Virginia	98
13	Minnesota	95
14	Kansas	87
15	Indiana	86
16	Mississippi	85
17	Pennsylvania	75
18	Kentucky	70
19	Colorado	65
20	New Mexico	64
21	Louisiana	61
22	Tennessee	57
23	Alabama	56
24	Wisconsin	51
25	Georgia	50
26	New Hampshire	50
27	South Dakota	50
28	North Dakota	49
29	Utah	48
30	Ohio	46
31	Arizona	43
32	California	42
33	Missouri	41
34	Michigan	38
35	Texas	32
36	Florida	30
37	Nevada	29
38	Washington	26
39	North Carolina	24
40	Oklahoma	23
41	South Carolina	22
42	Maine	18
43	Puerto Rico	17
44	Arkansas	16
45	Oregon	16
46	Wyoming	13
47	Vermont	11
48	West Virginia	10
49	Idaho	9
50	Alaska	2
51	Hawaii	0
52	Montana	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,009
2	New Jersey	928
3	Connecticut	738
4	Massachusetts	611
5	Louisiana	439
6	Michigan	418
7	District of Columbia	374
8	Rhode Island	335
9	Pennsylvania	250
10	Illinois	224
11	Maryland	213
12	Delaware	192
13	Indiana	180
14	Colorado	156
15	Georgia	120
16	Mississippi	114
17	Washington	113
18	Ohio	97
19	Nevada	89
20	Vermont	84
21	Virginia	83
22	Minnesota	80
23	New Mexico	77
24	Florida	68
25	New Hampshire	67
26	Missouri	66
27	Iowa	65
28	Alabama	64
29	Kentucky	62
30	Oklahoma	62
31	California	60
32	Wisconsin	60
33	South Carolina	57
34	Arizona	54
35	Kansas	54
36	Maine	45
37	North Carolina	45
38	Nebraska	42
39	Idaho	36
40	Tennessee	34
41	North Dakota	32
42	Texas	32
43	Arkansas	27
44	South Dakota	27
45	West Virginia	27
46	Oregon	26
47	Puerto Rico	17
48	Utah	17
49	Montana	14
50	Hawaii	12
51	Wyoming	12
52	Alaska	9

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Connecticut	18
2	New Jersey	18
3	Rhode Island	18
4	Massachusetts	17
5	New York	12
6	District of Columbia	11
7	Pennsylvania	10
8	Illinois	7
9	Maryland	7
10	Delaware	6
11	Louisiana	6
12	Michigan	5
13	Mississippi	5
14	Colorado	4
15	Indiana	4
16	Georgia	3
17	Iowa	3
18	Minnesota	3
19	New Mexico	3
20	Ohio	3
21	Virginia	3
22	Kansas	2
23	Nevada	2
24	Alabama	1
25	Arizona	1
26	Arkansas	1
27	California	1
28	Florida	1
29	Kentucky	1
30	Maine	1
31	Missouri	1
32	Nebraska	1
33	New Hampshire	1
34	North Carolina	1
35	South Carolina	1
36	South Dakota	1
37	Vermont	1
38	Washington	1
39	Wisconsin	1
40	Alaska	0
41	Hawaii	0
42	Idaho	0
43	Montana	0
44	North Dakota	0
45	Oklahoma	0
46	Oregon	0
47	Puerto Rico	0
48	Tennessee	0
49	Texas	0
50	Utah	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	119,816	1	99
Lincoln	Arkansas	65,494	2	99
Dakota	Nebraska	50,634	3	99
Nobles	Minnesota	49,424	4	99
Bledsoe	Tennessee	39,896	5	99
Davidson	Tennessee	4,786	211	93
Richland	South Carolina	2,381	536	82
Pierce	Washington	1,722	745	76
Orange	California	923	1220	61
York	South Carolina	779	1384	55

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Randolph	Georgia	2,951	1	99
Early	Georgia	2,650	2	99
Terrell	Georgia	2,462	3	99
Nassau	New York	1,658	4	99
Essex	New Jersey	1,651	5	99
Richland	South Carolina	113	467	85
Pierce	Washington	60	763	75
Davidson	Tennessee	46	895	71
Orange	California	19	1274	59
York	South Carolina	14	1357	56

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.